{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9e91da26-1cad-4dff-80a6-5465a1d07094",
   "metadata": {},
   "source": [
    "# Building Content Intelligence: A Multi-Platform Content Generation Agent with LangGraph\n",
    "\n",
    "## Overview\n",
    "This tutorial demonstrates how to build Content Intelligence, an advanced content generation agent using LangGraph. The agent is designed to transform input text into platform-optimized content across multiple social media channels, incorporating research-driven insights and maintaining brand consistency throughout the process.\n",
    "\n",
    "## Motivation\n",
    "Content creation for multiple platforms is a complex, time-consuming task that requires understanding platform-specific requirements, audience preferences, and maintaining consistent messaging. Content Intelligence automates this process by:\n",
    "- Analyzing and summarizing input content\n",
    "- Conducting research to enhance content quality\n",
    "- Generating platform-specific content while maintaining brand voice\n",
    "- Managing complex workflows through a structured graph approach\n",
    "\n",
    "## Key Components\n",
    "\n",
    "### 1. State Management\n",
    "The agent uses TypedDict and Pydantic models to maintain strict type safety and manage various states throughout the content generation process:\n",
    "\n",
    "```python\n",
    "class InputState(TypedDict):\n",
    "    text: str\n",
    "    platforms: list[Platform]\n",
    "\n",
    "class SumamryOutputState(TypedDict):\n",
    "    text: str\n",
    "    text_summary: str\n",
    "    platforms: list[Platform]\n",
    "\n",
    "class ResearchOutputState(TypedDict):\n",
    "    text: str\n",
    "    research: str\n",
    "    platforms: list[Platform]\n",
    "```\n",
    "\n",
    "These state definitions ensure clean data flow between different stages of the content generation process.\n",
    "\n",
    "### 2. Agent Nodes\n",
    "The system is composed of several specialized nodes:\n",
    "\n",
    "#### a. Summary Node\n",
    "- Processes input text to create concise summaries\n",
    "- Uses GPT-4 for high-quality content understanding\n",
    "- Maintains key messages while reducing content length\n",
    "\n",
    "#### b. Research Node\n",
    "- Conducts platform-specific research using Tavily Search\n",
    "- Generates relevant questions based on content context\n",
    "- Analyzes successful content patterns and trends\n",
    "\n",
    "#### c. Platform-Specific Nodes\n",
    "- Instagram: Focuses on visual storytelling and engagement\n",
    "- Twitter: Optimizes for brevity and viral potential\n",
    "- LinkedIn: Emphasizes professional insights and thought leadership\n",
    "- Blog: Creates comprehensive, SEO-optimized content\n",
    "\n",
    "## Agent Architecture\n",
    "\n",
    "### 1. Workflow Design\n",
    "The agent uses LangGraph's StateGraph to create a structured workflow:\n",
    "\n",
    "![ContentIntelligence](../images/contentIntelli.svg)\n",
    "\n",
    "### 2. Data Flow\n",
    "1. Input text enters the summary node for initial processing\n",
    "2. Summarized content moves to the research node for enhancement\n",
    "3. Intent matching distributes content to platform-specific nodes\n",
    "4. Each platform node generates optimized content\n",
    "5. Final content is combined and returned\n",
    "\n",
    "### 3. Platform-Specific Processing\n",
    "Each platform has dedicated prompts tailored to its unique requirements:\n",
    "\n",
    "```python\n",
    "instagram_prompt = ChatPromptTemplate.from_template(\"\"\"\n",
    "You are a creative social media strategist specializing in Instagram content.  \n",
    "\n",
    "**Input Details:**  \n",
    "1. Text: {text}  \n",
    "2. Research: {research}  \n",
    "\n",
    "Your task is to create an **Instagram post caption** with:\n",
    "- Engaging Caption\n",
    "- Hashtag Suggestions\n",
    "- Call-to-Action (CTA)\n",
    "- Emoji Usage\n",
    "...\n",
    "\"\"\")\n",
    "```\n",
    "\n",
    "## Implementation Benefits\n",
    "\n",
    "1. **Modularity**\n",
    "   - Easy to add new platforms or modify existing ones\n",
    "   - Independent optimization of each component\n",
    "   - Simple maintenance and updates\n",
    "\n",
    "2. **Quality Control**\n",
    "   - Type safety throughout the pipeline\n",
    "   - Consistent brand voice across platforms\n",
    "   - Research-backed content generation\n",
    "\n",
    "3. **Scalability**\n",
    "   - Parallel processing of platform-specific content\n",
    "   - Efficient handling of multiple content pieces\n",
    "   - Easy integration with existing systems\n",
    "\n",
    "## Educational Applications\n",
    "\n",
    "This agent serves as an excellent example for learning:\n",
    "1. **Graph-Based AI Systems**\n",
    "   - Understanding state management in complex workflows\n",
    "   - Implementing typed data flows\n",
    "   - Managing parallel processing paths\n",
    "\n",
    "2. **Prompt Engineering**\n",
    "   - Platform-specific prompt design\n",
    "   - Context management across multiple steps\n",
    "   - Maintaining consistency across different models\n",
    "\n",
    "3. **System Architecture**\n",
    "   - Building modular AI systems\n",
    "   - Managing complex workflows\n",
    "   - Implementing type-safe AI applications\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "Content Intelligence demonstrates the power of LangGraph in creating sophisticated content generation workflows. By combining state management, research capabilities, and platform-specific optimization, we've created a system that can efficiently handle complex content generation tasks while maintaining quality and consistency.\n",
    "\n",
    "This tutorial provides a foundation for building similar agents, whether for content generation or other domains requiring complex workflow management. The principles demonstrated here – modular design, type safety, and structured workflows – can be applied to various AI agent development scenarios.\n",
    "\n",
    "## Next Steps\n",
    "\n",
    "1. **Extend the Agent**\n",
    "   - Add more social media platforms\n",
    "   - Implement image generation capabilities\n",
    "   - Add content performance tracking\n",
    "\n",
    "2. **Optimize Performance**\n",
    "   - Fine-tune prompts for better results\n",
    "   - Implement caching for research results\n",
    "   - Add error handling and retry mechanisms\n",
    "\n",
    "3. **Enhance Features**\n",
    "   - Add A/B testing capabilities\n",
    "   - Implement feedback loops\n",
    "   - Add content scheduling functionality\n",
    "\n",
    "Remember, the key to building successful agents is understanding both the technical implementation and the specific requirements of your use case. This tutorial provides a framework that can be adapted and extended to meet various content generation needs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fe2983ef-e695-41ee-bf8e-5a25e8782ee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
    "%pip install langgraph langchain-community tavily-python langchain_openai langchain_core langchain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "961b1321-bb32-4d39-979b-2355bd20c34d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import getpass\n",
    "\n",
    "\n",
    "def _set_env(name: str):\n",
    "    if not os.getenv(name):\n",
    "        os.environ[name] = getpass.getpass(f\"{name}: \")\n",
    "\n",
    "\n",
    "_set_env(\"OPENAI_API_KEY\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "420873ee-88df-4796-bc43-e07b9d453a0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "_set_env(\"LANGCHAIN_API_KEY\")\n",
    "_set_env(\"LANGCHAIN_ENDPOINT\")\n",
    "_set_env(\"LANGCHAIN_PROJECT\")\n",
    "_set_env(\"LANGCHAIN_TRACING_V2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f9a48a24-d9d5-4761-bd6a-0bdd68edf82a",
   "metadata": {},
   "outputs": [],
   "source": [
    "_set_env(\"TAVILY_API_KEY\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "89238d6a-a831-48b8-9a4f-bc237e527b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_details = {\n",
    "  \"user_name\": \"LangGraph Team\",\n",
    "  \"business_name\": \"LangGraph\",\n",
    "  \"industry\": \"AI Tools and Frameworks\",\n",
    "  \"business_type\": \"Tech Startup\",\n",
    "  \"target_audience\": [\"AI Developers\", \"Machine Learning Enthusiasts\", \"Enterprise AI Teams\"],\n",
    "  \"tone\": \"Professional\",\n",
    "  \"objectives\": [\"Awareness\", \"Education\"],\n",
    "  \"platforms\": [\"LinkedIn\", \"Twitter\", \"Medium\"],\n",
    "  \"preferred_platforms\": [\"LinkedIn\", \"Twitter\"],\n",
    "  \"platform_specific_details\": {\n",
    "    \"twitter_handle\": \"@LangGraphAI\",\n",
    "    \"linkedin_page\": \"linkedin.com/company/langgraph\",\n",
    "    \"medium_page\": \"medium.com/langgraph\"\n",
    "  },\n",
    "  \"campaigns\": [\n",
    "    {\n",
    "      \"title\": \"Memory Management Module Launch\",\n",
    "      \"date\": \"2024-05-20\",\n",
    "      \"platform\": \"LinkedIn\",\n",
    "      \"success_metric\": \"1000+ Shares\"\n",
    "    }\n",
    "  ],\n",
    "  \"popular_hashtags\": [\"#LangGraph\", \"#MemoryManagement\", \"#AIFrameworks\"],\n",
    "  \"themes\": [\"Memory Management\", \"AI Agent Development\"],\n",
    "  \"short_length\": 280,\n",
    "  \"long_length\": 2000,\n",
    "  \"assets_link\": \"https://drive.google.com/drive/folders/langgraph-assets\",\n",
    "  \"colors\": [\"#1E88E5\", \"#FFC107\"],\n",
    "  \"brand_keywords\": [\"Innovative\", \"Efficient\"],\n",
    "  \"restricted_keywords\": [\"Buggy\", \"Outdated\"],\n",
    "  \"competitors\": [\"LangChain\", \"Pinecone\"],\n",
    "  \"competitor_metrics\": [\"Content Shares\", \"Follower Growth\"],\n",
    "  \"posting_schedule\": [\"Tuesday 10 AM\", \"Friday 3 PM\"],\n",
    "  \"formats\": [\"Carousel\", \"Technical Blog\"],\n",
    "  \"personal_preferences\": \"Use technical terms but keep explanations concise.\"\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5586bd13-10e4-4550-809c-3ecbaf3eb88b",
   "metadata": {},
   "source": [
    "# Let's Define States for the Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4d633733-557e-48e5-912f-219395c42d8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing_extensions import TypedDict, List, Literal\n",
    "from pydantic import BaseModel\n",
    "from langgraph.graph.message import MessagesState\n",
    "import operator\n",
    "from typing import Annotated\n",
    "\n",
    "Platform = Literal[\"Twitter\",\"Linkedin\",\"Instagram\", \"Blog\"]\n",
    "\n",
    "class InputState(TypedDict):\n",
    "    text: str\n",
    "    platforms: list[Platform]\n",
    "\n",
    "class SumamryOutputState(TypedDict):\n",
    "    text: str\n",
    "    text_summary: str\n",
    "    platforms: list[Platform]\n",
    "\n",
    "class ResearchOutputState(TypedDict):\n",
    "    text: str\n",
    "    research: str\n",
    "    platforms: list[Platform]\n",
    "\n",
    "class IntentMatchingInputState(TypedDict):\n",
    "    text: str\n",
    "    research: str\n",
    "    platforms: list[Platform]\n",
    "\n",
    "class FinalState(TypedDict):\n",
    "    contents: Annotated[list, operator.add]\n",
    "\n",
    "class GeneratedContent(TypedDict):\n",
    "    generated_content: str"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8a1f73f-4a30-4521-a0b5-9b819d8a8944",
   "metadata": {},
   "source": [
    "# Let's Define our Agent Nodes:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "241bef1c-f4c6-46d3-9d17-b6d270f93e14",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pydantic import BaseModel, Field\n",
    "from langchain.prompts import ChatPromptTemplate\n",
    "from langgraph.prebuilt import create_react_agent\n",
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "# some intitializations\n",
    "summ_model = ChatOpenAI(model = \"gpt-4o-mini\", temperature=0.6)\n",
    "\n",
    "model = ChatOpenAI(model = \"gpt-4o\", temperature=0.6)\n",
    "\n",
    "sumamry_prompt = ChatPromptTemplate.from_template(\"\"\"\n",
    "Taks: You need to give a summary of this given text. This summary will help the user to get the idea of the whole text. Do not miss anything important as this summary will take place in Research.\n",
    "\n",
    "Text:\n",
    " {text}\n",
    "\n",
    "\"\"\")\n",
    "\n",
    "research_agent_prompt = ChatPromptTemplate.from_template(\"\"\"\n",
    "You are a member of the Content Generation Team. Your primary task is to research and analyze the provided details to enhance the content creation process.\n",
    "\n",
    "Here are the client's details:\n",
    "{user_details}\n",
    "\n",
    "Below is the summary of the content for which the client wants to generate textual material:\n",
    "{text_summary}\n",
    "\n",
    "The client wants to create content for the following platforms:\n",
    "{platforms}\n",
    "\n",
    "Your task is to focus on content development enhancements. For each platform, generate onyl 2 questions :\n",
    "\n",
    "- Suggest best keywords or hashtags relevant to the platform and the content intent.\n",
    "- Identify key points or themes that should be highlighted or have been emphasized in previous posts.\n",
    "- Propose possible content elements or formats (e.g., lists, visuals, tone adjustments) tailored to the platform's audience and characteristics.\n",
    "- .... Anything which is enhances content\n",
    "\n",
    "\n",
    "Response Format:\n",
    "[\n",
    "question1\",\n",
    " question2\",...\n",
    "]\n",
    "\"\"\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ea5eb94c-4ec4-4692-b5a5-399a3ff54b78",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.tools import TavilySearchResults\n",
    "from langchain_core.messages import HumanMessage, SystemMessage\n",
    "from langchain_core.runnables import RunnableConfig\n",
    "from langgraph.types import Send\n",
    "\n",
    "research_tool = TavilySearchResults(\n",
    "    max_results=2,\n",
    "    search_depth=\"advanced\",\n",
    "    include_answer=True,\n",
    "    include_raw_content=True,\n",
    "    include_images=True,\n",
    ")\n",
    "\n",
    "class ReserachQuestions(TypedDict):\n",
    "    questions: List[str]\n",
    "\n",
    "def summary_text(state: InputState) -> SumamryOutputState:\n",
    "    print(\"******* Generating summary of the given text *************\")\n",
    "    summary = summ_model.invoke(state[\"text\"]).content\n",
    "    return {\"text\": state[\"text\"], \"platforms\": state[\"platforms\"], \"text_summary\": summary}\n",
    "\n",
    "def research_node(state: SumamryOutputState) -> ResearchOutputState:\n",
    "    print(\"******* Researching for the best content *************\")\n",
    "    input_ = {\"user_details\": user_details, \"text_summary\": state[\"text_summary\"], \"platforms\": state[\"platforms\"]}\n",
    "    res = model.with_structured_output(ReserachQuestions, strict=True).invoke(research_agent_prompt.invoke(input_))\n",
    "    response = research_tool.batch(res[\"questions\"])\n",
    "    research = \"\"\n",
    "    for i,ques in enumerate(res[\"questions\"]):\n",
    "        research += \"question: \" + ques + \"\\n\"\n",
    "        research += \"Answers\" + \"\\n\\n\".join([res[\"content\"] for res in response[i]]) + \"\\n\\n\"\n",
    "    \n",
    "    return {\"text\": state[\"text\"], \"platforms\": state[\"platforms\"], \"research\": research}\n",
    "\n",
    "def IntentMatching(state: ResearchOutputState):\n",
    "    print(\"******* Sending data to each Platfrom *************\")\n",
    "    # platform_nodes = []\n",
    "    # for platform in state[\"platforms\"]:\n",
    "    #     platform_nodes.append(Send(platform, {\"text\": state[\"text\"],\"research\": state[\"research\"], \"platform\": platform}))\n",
    "    # return platform_nodes\n",
    "    {\"text\": state[\"text\"],\"research\": state[\"research\"], \"platforms\": state[\"platforms\"]}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05492f32-92f4-4e2e-9bc3-b3b40cef21d3",
   "metadata": {},
   "source": [
    "# Let's Make Platform Specific Nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "46a33e5d-0fef-42ce-b7a2-3ebc55f6d741",
   "metadata": {},
   "outputs": [],
   "source": [
    "instagram_prompt = ChatPromptTemplate.from_template(\"\"\"\n",
    "You are a creative social media strategist specializing in Instagram content.  \n",
    "\n",
    "**Input Details:**  \n",
    "1. Text: {text}  \n",
    "2. Research: {research}  \n",
    "\n",
    "Your task is to create an **Instagram post caption** and provide the following:  \n",
    "- **Engaging Caption**: Write a compelling caption that aligns with the given text, highlights the key points, and uses an **inspirational or engaging tone** (as per the audience).  \n",
    "- **Hashtag Suggestions**: Suggest at least 10 hashtags that are **trending and relevant** to the content and target audience.  \n",
    "- **Call-to-Action (CTA)**: Include a specific action to encourage user engagement (e.g., comment, tag friends, visit website).  \n",
    "- **Emoji Usage**: Add appropriate emojis to make the caption lively and engaging, without overdoing it.  \n",
    "\n",
    "**Special Guidelines:**  \n",
    "1. Keep the caption within 2200 characters but aim for 150–300 characters for better engagement.  \n",
    "2. Ensure hashtags balance **broad reach (#FitnessGoals)** and **niche relevance (#EcoFitFashion)**.  \n",
    "3. Optimize for Instagram’s algorithm by starting with a **hook** (e.g., a question or statement).  \n",
    "\n",
    "**Response Format:**  \n",
    "Caption: [Your Instagram caption here]  \n",
    "Hashtags: [#hashtag1, #hashtag2, ...]  \n",
    "CTA: [Call-to-Action here]  \n",
    "\n",
    "\"\"\")\n",
    "\n",
    "twitter_prompt = ChatPromptTemplate.from_template(\"\"\"\n",
    "You are a social media expert tasked with crafting tweets that drive engagement on Twitter.  \n",
    "\n",
    "**Input Details:**  \n",
    "1. Text: {text}  \n",
    "2. Research: {research}  \n",
    "\n",
    "Your task is to create **Twitter content** with the following specifications:  \n",
    "- **Tweet**: Craft a tweet that conveys the essence of the text in **280 characters or less**, ensuring clarity, conciseness, and a conversational tone.  \n",
    "- **Hashtag Suggestions**: Include up to 3 hashtags that enhance visibility and are platform-specific.  \n",
    "- **Thread**: If the content cannot fit in a single tweet, create a **thread** with concise, numbered tweets that maintain flow and engagement.  \n",
    "\n",
    "**Special Guidelines:**  \n",
    "1. Start with a **strong hook** in the first tweet to grab attention.  \n",
    "2. Use one or two relevant keywords or phrases identified in the research.  \n",
    "3. Maintain a balance between **professional** and **relatable** language.  \n",
    "\n",
    "**Response Format:**  \n",
    "Tweet: [Your tweet here]  \n",
    "Hashtags: [#hashtag1, #hashtag2, ...]  \n",
    "Thread:  \n",
    "1. [First tweet in the thread]  \n",
    "2. [Second tweet in the thread]  \n",
    "...  \n",
    "\n",
    "\"\"\")\n",
    "\n",
    "linkedin_prompt = ChatPromptTemplate.from_template(\"\"\"\n",
    "You are a professional LinkedIn content creator, focused on crafting posts that establish thought leadership and build connections.  \n",
    "\n",
    "**Input Details:**  \n",
    "1. Text: {text}  \n",
    "2. Research: {research}  \n",
    "\n",
    "Your task is to create a **LinkedIn post** with the following details:  \n",
    "- **Post Content**: Write a professional, thoughtful post elaborating on the text, tailored to LinkedIn’s audience. Highlight the key takeaways or updates and use a **formal yet engaging tone**.  \n",
    "- **Hashtags**: Suggest up to 5 hashtags relevant to LinkedIn’s professional audience.  \n",
    "- **CTA**: Include a CTA encouraging engagement (e.g., “Share your thoughts,” “Let us know how you tackle this,” or “Visit our page for more”).  \n",
    "\n",
    "**Special Guidelines:**  \n",
    "1. Aim for **150–300 words**, focusing on storytelling and professional insights.  \n",
    "2. Structure the post with:  \n",
    "   - A **hook** to grab attention.  \n",
    "   - The main body with value-driven insights.  \n",
    "   - A concluding CTA.  \n",
    "3. Avoid using jargon unless contextually relevant.  \n",
    "4. Ensure hashtags are business-focused and professional.  \n",
    "\n",
    "**Response Format:**  \n",
    "Post: [Your LinkedIn post here]  \n",
    "Hashtags: [#hashtag1, #hashtag2, ...]  \n",
    "CTA: [Call-to-Action here]  \n",
    "\n",
    "\"\"\")\n",
    "\n",
    "blog_prompt = ChatPromptTemplate.from_template(\"\"\"\n",
    "You are a content writer specializing in blogs that captivate readers and provide actionable insights.  \n",
    "\n",
    "**Input Details:**  \n",
    "1. Text: {text}  \n",
    "2. Research: {research}  \n",
    "\n",
    "Your task is to create a **markdown-formatted blog post** with the following structure:  \n",
    "- **Title**: Create an eye-catching and SEO-friendly blog title.  \n",
    "- **Introduction**: Write an engaging opening paragraph that sets the context and hooks the reader.  \n",
    "- **Main Body**: Elaborate on the text using the research to provide insights, examples, and supporting details. Structure it into sections with headings (H2/H3).  \n",
    "- **Conclusion**: Summarize key takeaways and include a CTA encouraging readers to take the next step.  \n",
    "\n",
    "**Special Guidelines:**  \n",
    "1. Use a tone aligned with the target audience (e.g., casual for general readers, formal for professionals).  \n",
    "2. Optimize for SEO by incorporating keywords from the research naturally into the content.  \n",
    "3. Ensure readability by using bullet points, numbered lists, and short paragraphs.  \n",
    "4. Keep the blog **800–1500 words**.  \n",
    "\n",
    "**Response Format:**  \n",
    "```markdown\n",
    "# [Title of the Blog]  \n",
    "\n",
    "## Introduction  \n",
    "[Your introduction here]  \n",
    "\n",
    "## Section 1: [Heading]  \n",
    "[Content]  \n",
    "\n",
    "## Section 2: [Heading]  \n",
    "[Content]  \n",
    "\n",
    "## Conclusion  \n",
    "[Conclusion with CTA]  \n",
    "\n",
    "\"\"\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8942cbe7-7600-4c5d-81af-07d8542c54f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Insta(state: IntentMatchingInputState) -> FinalState:\n",
    "    if not \"Instagram\" in state[\"platforms\"]:\n",
    "        return {\"contents\": [\"\"]}\n",
    "    res = model.invoke(instagram_prompt.invoke({\"text\": state[\"text\"], \"research\": state[\"research\"]}))\n",
    "    return {\"contents\": [res.content]}\n",
    "\n",
    "def Twitter(state: IntentMatchingInputState) -> FinalState:\n",
    "    if not \"Twitter\" in state[\"platforms\"]:\n",
    "        return {\"contents\": [\"\"]}\n",
    "    res = model.invoke(twitter_prompt.invoke({\"text\": state[\"text\"], \"research\": state[\"research\"]}))\n",
    "    return {\"contents\": [res.content]}\n",
    "\n",
    "def Linkedin(state: IntentMatchingInputState) -> FinalState:\n",
    "    if not \"Linkedin\" in state[\"platforms\"]:\n",
    "        return {\"contents\": [\"\"]}\n",
    "    res = model.invoke(linkedin_prompt.invoke({\"text\": state[\"text\"], \"research\": state[\"research\"]}))\n",
    "    return { \"contents\": [res.content]}\n",
    "\n",
    "def Blog(state: IntentMatchingInputState) -> FinalState:\n",
    "    if not \"Blog\" in state[\"platforms\"]:\n",
    "        return {\"contents\": [\"\"]}\n",
    "    res = model.invoke(blog_prompt.invoke({\"text\": state[\"text\"], \"research\": state[\"research\"]}))\n",
    "    return { \"contents\": [res.content]}\n",
    "\n",
    "def combining_content(state:FinalState) -> GeneratedContent:\n",
    "    final_content = \"\"\n",
    "    for content in state[\"contents\"]:\n",
    "        final_content += content + \"\\n\\n\"\n",
    "    return {\"generated_content\": final_content}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a5ecfd5-553d-4cd9-9e34-c2c8bd81de4f",
   "metadata": {},
   "source": [
    "# Defining Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "51184d87-ac1b-4517-9693-d0fca62401e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_37931/1326417490.py:5: LangGraphDeprecationWarning: Initializing StateGraph without state_schema is deprecated. Please pass in an explicit state_schema instead of just an input and output schema.\n",
      "  builder = StateGraph(input=InputState, output=GeneratedContent)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<langgraph.graph.state.StateGraph at 0x7ac64868b210>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "\n",
    "from langgraph.graph import StateGraph, START, END\n",
    "\n",
    "builder = StateGraph(input=InputState, output=GeneratedContent)\n",
    "\n",
    "# Nodes\n",
    "builder.add_node(\"summary_node\",summary_text)\n",
    "builder.add_node(\"research_node\", research_node)\n",
    "builder.add_node(\"intent_matching_node\", IntentMatching)\n",
    "builder.add_node(\"instagram\", Insta)\n",
    "builder.add_node(\"twitter\", Twitter)\n",
    "builder.add_node(\"linkedin\", Linkedin)\n",
    "builder.add_node(\"blog\", Blog)\n",
    "builder.add_node(\"combine_content\", combining_content)\n",
    "\n",
    "\n",
    "# Flow\n",
    "builder.add_edge(START, \"summary_node\")\n",
    "builder.add_edge(\"summary_node\", \"research_node\")\n",
    "builder.add_edge(\"research_node\", \"intent_matching_node\")\n",
    "builder.add_edge(\"intent_matching_node\", \"instagram\")\n",
    "builder.add_edge(\"intent_matching_node\", \"twitter\")\n",
    "builder.add_edge(\"intent_matching_node\", \"linkedin\")\n",
    "builder.add_edge(\"intent_matching_node\", \"blog\")\n",
    "builder.add_edge(\"blog\", \"combine_content\")\n",
    "builder.add_edge(\"twitter\", \"combine_content\")\n",
    "builder.add_edge(\"instagram\", \"combine_content\")\n",
    "builder.add_edge(\"linkedin\", \"combine_content\")\n",
    "builder.add_edge(\"combine_content\", END)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e962cf49-0824-4707-92be-b544e5341c04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph = builder.compile()\n",
    "\n",
    "display(Image(graph.get_graph().draw_mermaid_png()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "47b3eb4f-3549-49c7-a346-da47b99270d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******* Generating summary of the given text *************\n",
      "******* Researching for the best content *************\n",
      "******* Sending data to each Platfrom *************\n"
     ]
    }
   ],
   "source": [
    "res = graph.invoke({\"text\": \"\"\"\n",
    "\n",
    "LangGraph provides a comprehensive memory management system that supports both short-term and long-term memory, enabling applications to retain and utilize information across interactions effectively. Here’s an overview of both types of memory:\n",
    "\n",
    "Short-Term Memory\n",
    "Definition: Short-term memory in LangGraph is designed to manage data within a single conversational thread. It allows the application to remember previous interactions during a session.\n",
    "\n",
    "Implementation:\n",
    "\n",
    "Short-term memory is managed as part of the agent's state, which is persisted using thread-scoped checkpoints. This means that the state can be saved and resumed, allowing for continuity in conversations.\n",
    "It typically includes conversation history, user inputs, and other relevant data that are necessary for maintaining context during interactions.\n",
    "Use Cases:\n",
    "\n",
    "Managing conversation history to provide context for ongoing interactions.\n",
    "Storing temporary data that is relevant only for the duration of a session, such as user preferences or recent queries.\n",
    "Challenges:\n",
    "\n",
    "Long conversations can lead to large memory usage, which may exceed the context window of language models. Techniques such as summarization or message trimming are often employed to manage this effectively.\n",
    "Long-Term Memory\n",
    "Definition: Long-term memory allows LangGraph applications to retain information across multiple conversational threads and sessions. This type of memory is essential for building personalized user experiences.\n",
    "\n",
    "Implementation:\n",
    "\n",
    "Long-term memory is organized into custom namespaces, allowing for hierarchical storage of information. Each memory is stored as a JSON document, making it easy to retrieve and manage.\n",
    "LangGraph supports various storage backends, including in-memory storage, databases, and other persistent storage solutions.\n",
    "Use Cases:\n",
    "\n",
    "Retaining user profiles, preferences, and historical interactions that can be referenced in future conversations.\n",
    "Storing structured information extracted from conversations, such as facts or knowledge triples, which can enhance the model's responses.\n",
    "Advantages:\n",
    "\n",
    "Long-term memory enables applications to provide a more personalized and context-aware experience by recalling past interactions and user-specific information.\n",
    "Conclusion\n",
    "LangGraph's memory management system is designed to handle both short-term and long-term memory effectively. Short-term memory focuses on maintaining context within a single session, while long-term memory allows for the retention of information across multiple sessions. This dual approach enhances the capabilities of LangGraph applications, enabling them to deliver more coherent and personalized interactions. By leveraging techniques such as namespaces and structured storage, LangGraph provides a flexible and powerful framework for managing memory in conversational AI applications.\n",
    "\n",
    "\n",
    "\"\"\", \"platforms\": [\"Twitter\",\"Blog\"]})"
   ]
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      "```markdown\n",
      "# Unleashing the Power of Memory Management in LangGraph for AI Excellence\n",
      "\n",
      "## Introduction  \n",
      "In the ever-evolving landscape of artificial intelligence, memory management plays a pivotal role in enhancing the capabilities of conversational applications. Enter LangGraph, a revolutionary framework designed to optimize memory usage for AI agents, enabling them to deliver more coherent, context-aware, and personalized interactions. Whether you're an AI developer or a tech enthusiast, understanding the intricacies of LangGraph's memory management system is crucial to harnessing its full potential. This blog delves into the dual approach of short-term and long-term memory in LangGraph, offering insights and actionable strategies to elevate your AI projects.\n",
      "\n",
      "## Section 1: Mastering Short-Term Memory in LangGraph  \n",
      "Short-term memory is the cornerstone of maintaining context within a single session, ensuring seamless interactions.\n",
      "\n",
      "### Definition and Implementation  \n",
      "LangGraph's short-term memory is engineered to manage data within a single conversational thread. By utilizing thread-scoped checkpoints, this system allows applications to remember previous interactions during a session, preserving continuity. This memory encompasses conversation history, user inputs, and other contextual data essential for maintaining the flow of dialogue.\n",
      "\n",
      "### Use Cases  \n",
      "- **Conversation History Management**: Retain dialogue context to enhance ongoing interactions.\n",
      "- **Session-Specific Data Storage**: Store temporary information like user preferences or recent queries relevant only for the session duration.\n",
      "\n",
      "### Challenges and Solutions  \n",
      "Long conversations often lead to increased memory usage, potentially exceeding the context window of language models. To address this, techniques such as summarization or message trimming are employed, ensuring efficient memory management without sacrificing context.\n",
      "\n",
      "## Section 2: Harnessing Long-Term Memory for Personalized Interactions  \n",
      "Long-term memory in LangGraph transcends single sessions, enabling applications to retain and utilize information across multiple interactions.\n",
      "\n",
      "### Definition and Implementation  \n",
      "LangGraph's long-term memory is structured using custom namespaces for hierarchical storage, with each memory stored as a JSON document. This setup facilitates easy retrieval and management, supporting various storage backends like in-memory storage and databases.\n",
      "\n",
      "### Use Cases  \n",
      "- **User Profile Retention**: Store user profiles, preferences, and historical interactions for future reference.\n",
      "- **Structured Knowledge Storage**: Retain facts or knowledge triples extracted from conversations to enrich responses.\n",
      "\n",
      "### Advantages  \n",
      "By recalling past interactions and user-specific information, long-term memory allows applications to deliver a more personalized and context-aware experience.\n",
      "\n",
      "## Section 3: Effective Strategies for Memory Management in LangGraph  \n",
      "To maximize the impact of LangGraph's memory management system, consider the following strategies:\n",
      "\n",
      "- **Leverage Custom Namespaces**: Organize long-term memory using custom namespaces for efficient hierarchical storage.\n",
      "- **Implement Summarization Techniques**: Use summarization to manage large conversation histories effectively.\n",
      "- **Utilize Persistent Storage Solutions**: Opt for databases or other persistent storage solutions for robust long-term memory management.\n",
      "\n",
      "## Conclusion  \n",
      "LangGraph's innovative memory management system bridges the gap between short-term and long-term memory, empowering AI applications to deliver richer, more personalized interactions. By understanding and implementing these memory strategies, developers can unlock new levels of AI excellence, transforming user experiences. Ready to elevate your AI projects? Explore LangGraph's memory management capabilities today and unleash the full potential of your conversational applications.\n",
      "```\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "**Tweet:**  \n",
      "Unlock the power of memory in AI with LangGraph! 🧠 Discover how short-term & long-term memory systems enhance personalized, context-aware interactions in your applications. Dive in and transform your AI experience today! #AIcommunity #AItools #LangGraph\n",
      "\n",
      "**Hashtags:**  \n",
      "[#AIcommunity, #AItools, #LangGraph]  \n",
      "\n",
      "**Thread:**  \n",
      "1. 🚀 Introducing LangGraph's dual memory system: Short-term memory manages session data, while long-term memory retains user info across threads, creating personalized experiences.  \n",
      "2. Short-term memory keeps conversation history & user inputs in check, ensuring seamless session continuity. Perfect for maintaining context!  \n",
      "3. Long-term memory, organized in namespaces, stores user profiles & past interactions, allowing for a richer, more personalized user experience.  \n",
      "4. Embrace the future of AI interactions with LangGraph's memory management system. Enhance your app's capabilities with structured storage and dynamic memory solutions! #AIchat\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(res[\"generated_content\"])"
   ]
  },
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    "# Additional Considerations: Discuss limitations, potential improvements, or specific use cases.\n",
    "\n",
    "## Limitations\n",
    "* This can generate only Textual content and only takes text as an input\n",
    "* It is not optimized with reviers and some other check which can be done by providing special techniques to each platform nodes.\n",
    "\n",
    "\n",
    "## Potential Improvements\n",
    "* Image processing ability\n",
    "* Taking inputs automatically and posting these content automatically\n",
    "* Adding a Chat interface to let user dicuss things in during research\n",
    "* Human Review before finalising it\n",
    "\n",
    "## Specific Use cases\n",
    "* generate texutal Content with language tone within seconds\n",
    "* Give insights over the content"
   ]
  },
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   "source": []
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